TOWARDS AN IMMUNE-INSPIRED TEMPORAL ANOMALY DETECTION ALGORITHM BASED ON TUNABLE ACTIVATION THRESHOLDS

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2009

Autores

Mario Antunes (Autor) Outra; Manuel Correia (Autor) FCUP; Jorge Carneiro (Autor) Outra

Ata de Conferência Internacional

Título: ARTIFICIAL NEURAL NETWORK APPROACH FOR OBESITY-HYPERTENSION CLASSIFICATION

Páginas: 357-362

BIOSTEC 2009 - Second International Joint Conference on Biomedical Engineering Systems and Technologies

Vila Nova de Gaia, Portugal, 14 a 17 de Janeiro de 2009

Indexação

ISI Web of Knowledge - 1 Citação

Scopus - 4 Citações

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Engenharia electrotécnica, electrónica e informática

Outras Informações

ID Authenticus: P-003-PA2

Abstract (EN): The detection of anomalies in computer environments, like network intrusion detection, computer virus or spam classification, is usually based on some form of pattern search on a database of "signatures " for known anomalies. Although very successful and widely deployed, these approaches are only able to cope with anomalous events that have already been seen. To cope with these weaknesses, the "behaviour" based systems has been deployed. Although conceptually more appealing, they have still an impractical high rate of false alarms. The vertebrate Immune System is an emergent and appealing metaphor for new ideas on anomaly detection, being already adopted some algorithms and theoretical theories in particular fields, such as network intrusion detection. In this paper we present a temporal anomaly detection architecture based on the Grossman's Tunable Activation Threshold (TAT) hypothesis. The basic idea is that the repertoire of immune cells is constantly tuned according to the cells temporal interactions with the environment and yet retains responsiveness to an open-ended set of abnormal events. We describe some preliminary work on the development of an anomaly detection algorithm derived from TAT and present the results obtained thus far using some synthetic data-sets.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6